Keynote speakers
Bibliographic record
Abstract
where he was both a Fulbright-LASPAU Scholar and an American Vacuum Society Scholar.His doctoral work on dynamic temperature measurements and combustion optimization in rocket and jet engines was sponsored and used by NASA, Cleveland, USA.Dr. Deen is regarded as the world's foremost authority in modeling and noise of electronic and optoelectronic devices.He has successfully transferred powerful engineering and circuit models for high-performance semiconductor devices to several companies.His practical models and experimental innovations for reliability prediction have contributed significantly to the design and manufacture of reliable high performance photodetectors.Dr. Deen's research record includes more than 480 peer-reviewed articles and six patents that have been used in industry.He is the author/editor of 20 books and conference proceedings, the textbook Silicon Photonics -Fundamentals and Devices, 16 invited book chapters, and has received 12 best paper/poster awards.Over his career, he has won more than fifty awards and honors.Dr. Deen's peers have elected him to Fellow status in nine national academies and professional organizations, including The Royal Society of Canada (RSC), The American Physical Society and The Electrochemical Society.His other awards and honors include the Callinan Award and the Electronics and Photonics
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.473 | 0.271 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".